Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids. On one hand, coordination among sources is facilitated by distributed, privacy-minded processing at multiple locations, whereas on the other hand, it also creates exogenous data arrival paths for adversaries that can lead to cyber-physical attacks amongst other reliability issues in the communication layer. This long-standing problem necessitates new intrinsic ways of exchanging information between converters through power lines to optimize the system's control performance. Going beyond the existing power and data co-transfer technologies that are limited by efficiency and scalability concerns, this paper proposes neuromorphic learning to implant communicative features using spiking neural networks (SNNs) at each node, which is trained collaboratively in an online manner simply using the power exchanges between the nodes. As opposed to the conventional neuromorphic sensors that operate with spiking signals, we employ an event-driven selective process to collect sparse data for training of SNNs. Finally, its multi-fold effectiveness and reliable performance is validated under simulation conditions with different microgrid topologies and components to establish a new direction in the sense-actuate-compute cycle for power electronic dominated grids and microgrids.

翻译：先进通信、计算与人工智能的协同正为微电网的协调运行与韧性开辟新方向。一方面，分布式、注重隐私的跨节点处理促进了源端协调，另一方面，这也为攻击者创造了外生数据注入路径，可能引发通信层的网络物理攻击及其他可靠性问题。这一长期存在的难题要求通过电力线在变流器之间交换信息的新内在方式，以优化系统的控制性能。为突破现有功率与数据共传技术在效率与可扩展性方面的局限，本文提出利用尖峰神经网络（SNNs）在各节点植入神经形态学习特性，通过节点间的功率交换以在线协作方式训练。与传统的基于尖峰信号的神经形态传感器不同，我们采用事件驱动的选择性过程来收集稀疏数据，用于训练SNNs。最后，在不同微电网拓扑与组件的仿真条件下，验证了该方法的多维有效性与可靠性能，为电力电子主导的电网与微电网的感知-决策-执行循环确立了新方向。